The construction industry is undergoing a digital transformation, and one of the most significant changes is the adoption of artificial intelligence (AI) technologies. AI is being used to improve efficiency, safety, and quality in construction projects. In this blog post, we will explore how AI is changing the construction industry in 2023. AI is being used in various ways in construction projects. For example, drones are being used to survey construction sites and collect data.

Deepfakes leverage powerful techniques from machine learning and artificial intelligence to generate visual and audio content with a such a high degree of realism that it has enormous potential to deceive. This article in Medium explores efforts into research and development into creating countermeasures to such bogus content. Within recent months, a number of mitigation mechanisms have been proposed and cited with the use of Neural Networks and Artificial Intelligence being at the heart of them. From this, we can distinguish that a proposal for technologies that can automatically detect and assess the integrity of visual media is therefore indispensable and in great need if we wish to fight back against adversarial attacks. (Nguyen, 2019)

Katherine Bindley of the Wall Street Journal is at CES to take a look at the latest AI-infused cameras on the market. Two new smart systems use cameras, artificial intelligence and an assortment of sensors to keep watch over you--Patscan looks for threats in public spaces, while Eyeris monitors the driver and passengers in a car. WSJ's Katherine Bindley visits CES to explores their advantages, as well as their privacy costs. Back @Microsoft to help customers leverage #AI Opinions mine. I blog to help you become a better data scientist/ML engineer Opinions are mine.

Recently, I was granted access to the LinkedIn Live beta program and here is the first live stream I did on the platform. With me is Andy Leonard and talk about AI, ethics, MSDN Magazine, and LinkedIn Live. Back @Microsoft to help customers leverage #AI Opinions mine. I blog to help you become a better data scientist/ML engineer Opinions are mine. This site uses Akismet to reduce spam.

Data scientists need the ability to explain their models to executives and stakeholders, so they can understand the value and accuracy of their findings. The ability to interpret a generated model is crucial to ensure compliance with company policies, industry standards, and government regulations. Here's an interesting write up on Model Interpretability in Azure Machine Learning Services. Model designers and evaluators can use interpretability output of a model to verify hypotheses and build trust with stakeholders. They also use the insights into the model for debugging, validating model behavior matches their objectives, and to check for bias or insignificant features.

Machine Learning can be confusing sometimes. From the esoteric terms to elevated expositions it seems like a terribly difficult area to get into. Seth Juarez, like me, started off as a developer, and he tackles the one term that is used all of the time in Machine Learning: the elusive "model. First we set up how machine learning is different, how to think about it, and finally what a model actually is (spoiler alert – think "a function written a different way"). Back @Microsoft to help customers leverage #AI Opinions mine.

What exactly is the difference is between Keras and TensorFlow? Technically speaking, you could use Keras with a variety of potential backends. But what exactly does that mean? Basically, you are able to make any Keras call you need from within TensorFlow. You get to enjoy the TensorFlow backend, while leveraging the simplicity of of Keras.

Vijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present. This conversation is part of the Artificial Intelligence podcast run by Lex Fridman.

Here's a great talk from Build 2019 about the importance of design in creating Voice and chat virtual assistants. Designing a natural language interface can be difficult, is the interface supposed to be able to interpret every single nuance of speech? Or should we aim more towards forced language and make our users learn how to interact with simple commands? All the big companies are making huge investments in AI personal assistants. Amazon has Alexa, Google has Google assistant, Apple has Siri and Microsoft has Cortana to name a few.

Understanding software development procedures in 2019 is vital. With many new programming languages approaching the matter, the way we build programs, machines and everything in between changes on a daily basis. With this in mind, a common denominator in software builds, especially for what concerns mobile apps, has been Artificial Intelligence. It's no secret that automation and machine code have both been incredibly big from a development and business perspective and their further simplification in order for them to be applied to a variety of pieces of software is at a groundbreaking state nowadays. Let's analyse the matter in more detail.